This interdisciplinary project aims to develop an innovative rehabilitation technology for prevention of fall in elderly and more specifically elderl with diabetes by combining wearable sensor technology and virtual reality. Falls in the elderly are a major geriatric problem, with an estimated 30% of elderly adults over 65 years of age falling each year. [1-3] The direct and indirect societal costs of falls are enormous. Among elderly people in the US alone, the cost of falls has been estimated to be US$20 billion per year. [4] Accurate identification of those participants at high risk of falls would facilitate appropriat and timely intervention, and could lead to improved quality of care and reduced associated hospital costs, due to reduced admissions and reduced severity of falls. The prevalence of diabetes is steadily increasing in elderly people. Some of its under-appreciated complications, such as impaired physical functioning and increased risk of falls and fractures, have not been thoroughly investigated well. Diabetes prevalence is estimated to increase to 33% in US [5]. Insulin use is associated with a 90% increased amputation risk [6] and 2.8 increased fall risk [7]; an important emerging risk factor for foot ulcer non-healing.[8] Approximately 50% of patients with diabetes show evidence of diabetic neuropathy, making this the most common symptomatic complication. [9] People with diabetes-related peripheral neuropathy (DPN) frequently exhibit concomitant postural instability that can lead to falls, fracture, depression, anxiety, and decreased quality of life. Individuals with diabetes are 2.5-fold more likely to experience an accidental fall or a fall-related injury than healthy ones [10]. This is an often neglected problem, and has received little attention regarding development of and testing of innovative strategies to improve balance and postural stability in this patient population. The proposed technology utilizes wearable sensors, similar to those used in an iPhone(R), to provide real time information about a subject's postural stability and motor adaptation ability during gait in a virtual environment. Three important components of the proposed strategy are, 1) to assess and improve the perception of lower extremity position during challenging conditions; 2) to motivate and guide simple exercise performance in a clinic/home, using an interactive virtual reward-based scheme; and 3) to improve postural control in patients with altered sensory feedback condition. The information gathered by the proposed technology provides novel opportunities to develop effective rehabilitation strategies to improve balance and postural stability in diabetes/DPN patients. In the first phase of this study, we will improve the overall architecture of our initial prototype system for measuring lower extremity position in real time using wearable technology. The designed prototype will be tested in the context of two clinical studies to demonstrate its accuracy for assessing balance and lower extremity position as well as its sensitivity and specificity to diagnose neuropathy complication in older adults. We hypothesize that amplifying visual perception of the lower extremity position during a set of obstacle crossing tasks in a safe virtual environment will help patients better coordinate their postural control, quality of life and gait steadiness. After proof of concept study proposed in thi phase, we will conduct a large clinical study in phase II of the project to demonstrate the benefit of the proposed technology in improving gait and balance in elderly with diabetes. The proposed technology has a very wide range of applications in elderly care, stroke, neurodegenerative diseases, traumatic brain injury (TBI), and in treating balance disorders and dizziness. PUBLIC HEALTH RELEVANCE: Obstacle reconciliation can be compromised in people with impaired gait due to age, stroke, diabetes and Parkinson's disease amongst various others. In certain cases the impaired lower extremity position judgment - mainly due to impaired proprioceptive feedback (e.g. in patients suffering from diabetes and neuropathy) - can cause obstacle collision leading to falls or even serious injuries. We propose a simple modality to measure as well as train balance strategies in patients with impairment of lower extremity perception, which provides real time visual feedback to the subject.